Development of the Mugla Score: An Association-Based Tool for Risk Stratification in Emergency Department Patients with Rhabdomyolysis

Development of the Mugla Score: An Association-Based Tool for Risk Stratification in Emergency Department Patients with Rhabdomyolysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of the Mugla Score: An Association-Based Tool for Risk Stratification in Emergency Department Patients with Rhabdomyolysis Omer Faruk Karakoyun, Fulden Cantaş Türkiş, Yalcin Golcuk, Mehmet Reha Yılmaz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6185742/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Internal and Emergency Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Rhabdomyolysis is a potentially life-threatening syndrome characterized by skeletal muscle breakdown and systemic release of intracellular components, often resulting in acute kidney injury or death. Early risk stratification remains challenging in the emergency department (ED) setting due to heterogeneous presentations and unpredictable outcomes. Objective: To develop and internally validate the Mugla Score—a pragmatic, association-based tool for predicting adverse outcomes in ED patients with rhabdomyolysis. Methods: In this retrospective, single-center cohort study, adult ED patients with serum creatine kinase ≥1000 U/L between July 1, 2019, and July 1, 2024, were included. The primary outcome was a composite of renal replacement therapy or 90-day mortality. Multivariable logistic regression identified independent predictors, which were assigned weighted point values. Internal validity was assessed using five-fold cross-validation and 1,000-iteration bootstrap resampling. Results: Among 1,031 patients (mean age: 49.0 ± 21.8 years; 75.9% male), 109 (10.6%) experienced the composite outcome. Seven variables were independently associated with adverse events: age ≥50 years, platelet count ≤170 ×10³/μL, MCHC ≤32.8 g/dL, calcium ≤8.5 mg/dL, ALP ≥115 U/L, BEecf ≤−6 mmol/L, and etiological classification. The Mugla Score (range: 0–12.5) showed strong discrimination (AUC: 0.861, 95% CI: 0.824–0.898). A threshold of ≥4 points yielded a 97% negative predictive value. Conclusions: The Mugla Score provides a clinically interpretable, ED-focused tool for early risk stratification in rhabdomyolysis. While internally validated, external prospective studies are needed to assess generalizability prior to routine clinical adoption. rhabdomyolysis acute kidney injury renal replacement therapy emergency department scoring system prognosis Figures Figure 1 Figure 2 Figure 3 1. Introduction Rhabdomyolysis is a complex clinical syndrome defined by the pathological release of intracellular constituents into the systemic circulation secondary to skeletal muscle injury {Stanley M, In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing`; 2024 Jan-. #1}{Stanley M, In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing`; 2024 Jan-. #1}. This syndrome can be precipitated by a myriad of etiologies, both traumatic and non-traumatic in nature. Diagnostic confirmation is typically achieved when serum creatine phosphokinase (CK) levels exceed 1000 U/L, a pivotal biomarker indicative of extensive myocyte destruction. The underlying pathophysiology involves the disintegration of muscle fibers, culminating in the efflux of myoglobin, electrolytes, and other intracellular components into the bloodstream. This cascade of events can precipitate a diverse array of clinical outcomes, ranging from benign asymptomatic CK elevation to severe, potentially life-threatening complications such as acute kidney injury (AKI) and, in extreme cases, mortality [1,2]. Rhabdomyolysis presents significant clinical challenges for Emergency Physicians (EPs) due to its potential for rapid progression to life-threatening complications if not promptly recognized and managed [3]. Among these complications, AKI is particularly concerning, as it frequently necessitates renal replacement therapy (RRT). Although several scoring systems exist for predicting complications, there remains a critical need for more comprehensive models that incorporate both etiological and laboratory data to enhance the prediction of adverse outcomes, including RRT and mortality [4,5]. In response to these challenges, we aimed to develop a novel scoring system specifically designed for the Emergency Departments (EDs), facilitating early identification of patients at high risk for both requiring RRT and all-cause mortality. 2. Methods 2.1. Study Design and Setting This retrospective, single-center, cross-sectional cohort study was conducted at the ED of a university-affiliated training and research hospital in [ blinded for peer review ], Turkey, from July 1, 2019, to July 1, 2024. The hospital, with a 50-bed ED and approximately 150,000 annual visits, offered an ideal setting for developing rhabdomyolysis assessment tools. Ethical approval was granted by the Institutional Review Board of [ blinded for peer review ] University (decision number: 240035/12). The study adhered to the principles outlined in the Declaration of Helsinki. Due to the retrospective nature of the analysis, the requirement for obtaining written informed consent from patients was waived. 2.2. Selection of Participants The inclusion criteria consisted of all patients aged 18 years or older who were diagnosed with rhabdomyolysis, confirmed by serum CK levels exceeding 1000 IU/L upon admission to the ED. The exclusion criteria included patients younger than 18 years, those with elevated CK levels due to acute coronary syndrome, cerebrovascular infarction, or bleeding, as well as patients who developed rhabdomyolysis post-admission from pre-existing conditions or iatrogenic causes. Additionally, patients with end-stage renal disease requiring RRT, those transferred out of the hospital, lost to follow-up, or with incomplete data were excluded. As this study retrospectively included all eligible patients during the specified period, a formal power analysis was not performed. 2.3. Definitions and Determination Etiological Factor Groups Rhabdomyolysis was defined by a serum CK level exceeding 1000 IU/L, indicative of significant muscle injury. Renal replacement therapy encompassed peritoneal dialysis, hemodialysis, or continuous RRT. Adverse outcomes were defined as the the initiation of RRT or all-cause mortality within a 3-month period. Rhabdomyolysis is a multifactorial condition with numerous potential etiologies, and currently, no universally accepted classification system exists. In our study, etiological factors were systematically categorized into five distinct classifications to enhance the clarity and precision of our analysis [1,6]. Group 1 encompassed etiological factors such as alcohol consumption, endocrine abnormalities, metabolic derangements, myopathies, and infections, including COVID-19. Group 2 involved trauma-related causes, surgical procedures, seizure activity, and extreme physical exertion. Group 3 encompassed hyperosmolar conditions, specifically general medical states associated with dehydration, including prolonged exposure to elevated temperatures and insufficient fluid intake. Group 4 comprised rhabdomyolysis induced by pharmacologic agents, exposure to various venoms, including those from snakes and scorpions, as well as environmental toxins. Group 5 encompassed factors that did not fit within the predefined categories and were thus classified as "other." 2.4. Data Collection Potentially eligible patient encounters were identified through a comprehensive query of the records database at [ blinded for peer review ] Training and Research Hospital. Data collection was meticulously performed using a standardized electronic spreadsheet, ensuring precise and thorough documentation of all pertinent variables. Upon ED admission, data were systematically recorded, including patient demographics and the etiological factors associated with rhabdomyolysis. Additionally, comprehensive ancillary data were gathered, encompassing initial laboratory results (e.g., complete blood count, serum chemistry, and blood gas analysis) as well as clinical seconder outcome measures such as hospital admission status and length of stay (LOS). The composite outcomes, including the need for RRT or 3-month all-cause mortality, were systematically documented for each patient. 2.5. Statistical Analysis The normality of the data was assessed using the Kolmogorov-Smirnov test. Continuous variables were presented as mean ± standard deviation (SD) for normally distributed data or as median (minimum–maximum) for non-normally distributed data, while categorical variables were expressed as frequencies and percentages. Comparative analyses between the good and adverse outcome groups were conducted utilizing the Mann-Whitney U test for continuous variables due to non-normal distribution, while categorical variables were analyzed with the χ² test. To develop a novel predictive score, initial univariate analyses were conducted to explore the relationships among demographic characteristics, etiological factors, and laboratory parameters. Based on these findings, optimal cut-off values for statistically significant variables were determined using Receiver Operating Characteristic (ROC) analysis, with Youden’s index employed to maximize sensitivity and specificity. Subsequently, the statistically significant variables that met the established cut-off values were incorporated into a multivariate logistic regression model, utilizing forward stepwise selection based on the Wald test to identify independent predictors of adverse outcomes. The selection criteria included an entry threshold of p <0.05 and a retention threshold of p <0.10, ensuring the robustness of the final model while minimizing the risk of overfitting. Consequently, a novel score was derived from this comprehensive logistic regression model. To enhance clinical interpretability, the regression coefficients of statistically significant predictors were standardized by dividing each coefficient by the smallest significant predictor coefficient and rounding to the nearest 0.5. Non-significant predictors were assigned a score of zero, and the cumulative score for each patient was computed by summing the individual predictor scores, resulting in a clinically relevant scoring system. Following this, ROC curve analysis was conducted to assess the utility of the novel score and to determine the optimal cut-off value. At this threshold, the positive predictive value (PPV) and negative predictive value (NPV) were calculated. In addition to this analysis, the model's internal validity was further assessed through the application of k-fold cross-validation (k = 5), which provided a robust measure of predictive consistency across multiple subsets of the dataset, thereby strengthening the overall findings of the study. All statistical tests were performed as two-tailed, with a p -value <0.05 considered statistically significant. Analyses were conducted using SPSS software version 27.0 (IBM Corp., Armonk, NY, USA) for general statistical analyses, MedCalc (MedCalc Software Ltd., Ostend, Belgium) for ROC analysis, and MATLAB (The MathWorks, Inc. MATLAB, version 9.11 (R2021b). Natick, Massachusetts: The MathWorks Inc., 2021) for k-fold cross-validation. 3. Results This study analyzed data from 1,031 patients diagnosed with rhabdomyolysis, with a mean age of 49.0± 21.8 years (range: 18–99 years). The majority of the participants were male, comprising 75.9% of the population. The predominant etiological groups linked to rhabdomyolysis were Group 2 (39.4%) and Group 5 (33.2%). Composite adverse outcomes occurred in 109 patients, representing 10.6% of the cohort. Of the entire cohort, 56 patients (5.5%) required RRT, and 84 patients (8.1%) succumbed within the 3-month follow-up period. Notably, the median survival time for those who died was 4 days (range: 1–78 days). Patients with adverse outcomes had a significantly higher median age of 72 years (18–99) compared to 44 years (18–98) in those with good outcomes (p <0.001). The median CK level for the entire cohort was 2627 U/L (range: 1000–54,986 U/L). There was no statistically significant difference in CK levels between outcome groups: 1549 U/L (1000–54,986 U/L) for good outcomes and 1538 U/L (1000–16,636 U/L) for adverse outcomes (p = 0.666). The hospitalization rate among patients was 53.1% (n = 545), with 14.4% (n = 148) of these requiring admission to the ICU. The median LOS for hospitalized patients was 7 days (range: 1–144 days), while the median LOS for ICU admissions was 6 days (range: 1–144 days). Table 1 provides a comprehensive overview of the baseline demographic characteristics, etiological factors, laboratory parameters, and secondary outcomes of the study population, stratified by outcome status at the end of the 3-month follow-up. To construct a predictive score, univariate analyses were initially conducted to identify significant variables associated with adverse outcomes. Subsequently, ROC curve analysis was employed to establish optimal cut-off values for the identified candidate predictors, with results summarized in Table 2. In the following phase, a multivariate logistic regression model was developed, with details presented in Table 3. This final model integrated several variables: age (≥50 years), platelet count (≤170 x 10³/μL), Mean Corpuscular Hemoglobin Concentration (MCHC) (≤32.8 g/dL), calcium levels (≤8.5 mg/dL), Alkaline Phosphatase (ALP) levels (≥115 U/L), Base Excess of extracellular fluid (BEecf) (≤-6 mmol/L), and relevant etiological factors from Groups 1 and 3. The final model, named the Mugla Score in reference to the location of its development, provides a comprehensive assessment of patient risk. The score ranges from 0 to 12.5, with the specific variables and their respective point allocations outlined in Table 4. ROC analysis determined that a Mugla Score of ≥4 was optimal for predicting adverse outcomes, exhibiting a sensitivity of 75.0%, specificity of 75.3%, and an AUC of 0.861 (95% CI: 0.824–0.898; p <0.001) as shown in Figure 1. At this threshold, the score demonstrated a PPV of 39% and a NPV of 97% ( p <0.001). The internal validity of the model was further assessed through 5-fold cross-validation, resulting in an AUC of 0.833 (95% CI: 0.796-0.866; p <0.001), with a sensitivity of 92.77% (95% CI: 84.9-97.3%) and a specificity of 64.84% (95% CI: 59.8-69.6%). Additionally, the PPV was 36.32%, while the NPV was 97.65%. 4. Discussion This study introduces the Mugla Score, a prognostic tool designed to predict adverse outcomes in patients with rhabdomyolysis in the ED. Incorporating demographic factors, such as age, and etiological considerations alongside key laboratory parameters—including platelet count, MCHC, calcium levels, ALP, and BEecf—the score exhibits substantial clinical utility in predicting the need for RRT and assessing three-month mortality risk. The Mugla Score provides a robust framework for identifying high-risk rhabdomyolysis patients, enhancing clinical decision-making and enabling targeted interventions to improve outcomes. Research on the impact of CBC parameters on adverse outcomes in rhabdomyolysis is limited. However, the simultaneous decrease in platelet count and MCHC indicates complex pathophysiological mechanisms. The link between ferrihemate, an endogenous breakdown product of myoglobin, and reductions in these parameters is primarily indirect, resulting from oxidative stress and systemic inflammation rather than direct effects. Under acidic conditions, ferrihemate generates free radicals that lead to oxidative damage and inflammation, which can activate and consume platelets, causing thrombocytopenia [7,8]. Similarly, reduced MCHC levels are associated with oxidative stress and hypoxia, impacting red blood cell stability and hemoglobin synthesis [9]. This instability can result in hemolysis, further altering MCHC and reflecting the extent of inflammation and cellular injury. Thus, monitoring platelet count and MCHC in patients with rhabdomyolysis may provide important insights into disease severity and the risk of AKI, highlighting their role as indicators of systemic complications. The association between ALP levels and rhabdomyolysis is receiving growing attention, with significant implications for both prognostic assessment and therapeutic strategy [10,11]. Elevated ALP shows a strong correlation with rhabdomyolysis severity, supporting its inclusion in the Mugla Score as a biomarker of disease burden. The pathophysiological basis for ALP elevation in this context is likely multifactorial, involving muscle cell injury, metabolic disturbances, and systemic inflammatory responses [12]. Additionally, metabolic dysregulation and oxidative stress, commonly observed in rhabdomyolysis, may further increase ALP levels, possibly reflecting a hepatic response to systemic injury. Hypocalcemia, also included in the Mugla Score, may result from calcium binding to myoglobin and other proteins released during muscle breakdown. Elevated ALP and low calcium levels may further indicate systemic inflammation and tissue hypoxia, both of which are prevalent in severe rhabdomyolysis cases [13, 14]. Incorporating both ALP and hypocalcemia into clinical assessments could thus improve early therapeutic decision-making, enable the anticipation of complications, and ultimately enhance patient outcomes in the ED. The relationship between acid-base disturbances, particularly metabolic acidosis, and rhabdomyolysis is crucial in predicting patient outcomes, as metabolic acidosis is consistently linked to poor prognosis in critically ill patients, a well-documented association in the literatüre [15, 16]. A reduction in base excess serves as a robust predictor of adverse outcomes in rhabdomyolysis, highlighting the necessity for prompt identification and correction of metabolic derangements. Integrating acid-base parameters into the Mugla Score augments its prognostic precision, emphasizing the pivotal role of metabolic correction in optimizing clinical outcomes. This approach facilitates the early detection of potential complications which can significantly improve patient recovery trajectories. In contrast to previous studies, our analysis did not identify trauma-related etiologies—commonly associated with direct muscle injury—as significant predictors of adverse outcomes in rhabdomyolysis [4, 17]. Instead, dehydration emerged as a key determinant in the Mugla Score, contributing to a higher points and reflecting its significant prognostic value. This finding aligns with existing literature linking volume depletion to worse outcomes in rhabdomyolysis, emphasizing dehydration's detrimental impact on renal function and systemic stability. Early fluid management, therefore, plays a critical role in mitigating the adverse effects of dehydration and improving patient outcomes. Furthermore, factors such as alcohol consumption, endocrine disorders, metabolic disturbances, myopathies, and infections—commonly recognized in the literature as contributors to rhabdomyolysis—were included as predictors of adverse outcomes [18-22]. These associations reinforce the multifactorial etiology of rhabdomyolysis, underscoring the importance of comprehensive patient evaluation for effective risk stratification. The adaptability of the Mugla Score to diverse clinical scenarios further enhances its utility in EDs, facilitating more precise risk prediction and management across varied patient populations. This flexibility is particularly relevant given recent global health challenges, notably the COVID-19 pandemic, which had a significant impact on our study population and period. The increased strain on healthcare systems, compounded by a higher incidence of related complications, underscores the urgent need for adaptive prognostic models that account for the broad spectrum of etiological factors influencing rhabdomyolysis. The McMahon Score, focused on patients with initial CK levels exceeding 5,000 U/L, is widely recognized as a cornerstone in the prognostic evaluation of rhabdomyolysis, demonstrating robust predictive accuracy for the need for RRT and mortality [4]. However, its utility remains largely restricted to those with markedly elevated CK levels, potentially overlooking a subset of patients at significant risk for adverse outcomes despite presenting with lower CK values. The Mugla Score has several strengths and the potential for broad acceptance, offering a significant advancement over existing models by providing a more comprehensive and nuanced approach to prognostication in rhabdomyolysis. By incorporating a diverse array of clinical parameters, it facilitates the assessment of a broader spectrum of disease severity, effectively including patients with CK levels below 5,000 U/L. Limitations This study provides a valuable advancement in rhabdomyolysis prognosis through the development of the Mugla Score, yet it is important to acknowledge its limitations. First, the retrospective, single-center design may constrain the broader applicability of these findings. Second, the Mugla Score could not be validated in external patient populations; however, to ensure internal validity, we employed stratified k-fold cross-validation, a robust method for internal model validation. Third, the variability in timing of patient presentation following injury, along with the lack of routine CK measurements, may have resulted in both underestimation and undiagnosed cases of rhabdomyolysis, potentially influencing the predictive accuracy of our model. 5. Conclusion The Mugla Score, a newly developed and internally validated prognostic tool, offers a substantial advancement in assessing rhabdomyolysis outcomes, providing an effective framework for predicting adverse events among patients presenting to the ED. With an established cutoff score of 4, this tool accurately identifies patients at elevated risk of severe complications, including the need for RRT and three-month mortality. Multicenter studies and prospective cohort analyses are warranted to externally validate this score and confirm its applicability across varied clinical settings and patient populations. Declarations Competing Interests: The authors declare that they have no conflicts of interest related to this work. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT Author Statement Omer Faruk Karakoyun (0000-0002-4476-7989), Fulden Cantas Turkis (0000-0002-7018-7187), Yalcin Golcuk (0000-0002-8530-8607), Mehmet Reha Yılmaz (0009-0005-9152-0662), and Burcu Kaymak Golcuk (0000-0003-2293-697X) contributed to this study as follows: Conceptualization: Omer Faruk Karakoyun, Yalcin Golcuk Methodology: Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk Software: Burcu Kaymak Golcuk, Mehmet Reha Yılmaz Validation: Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk Formal analysis: Omer Faruk Karakoyun, Mehmet Reha Yılmaz, Fulden Cantas Turkis, Yalcin Golcuk Investigation: Omer Faruk Karakoyun, Mehmet Reha Yılmaz, Burcu Kaymak Golcuk Resources: Yalcin Golcuk, Burcu Kaymak Golcuk Data Curation: Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk Writing - Original Draft: Omer Faruk Karakoyun, Burcu Kaymak Golcuk, Yalcin Golcuk, Writing - Review & Editing: Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk, Mehmet Reha Yılmaz, Burcu Kaymak Golcuk Visualization: Omer Faruk Karakoyun, Halil Emre Koyuncuoglu, Fulden Cantas Turkis, Yalcin Golcuk Supervision: Yalcin Golcuk References Stanley M, Chippa V, Aeddula NR, et al. 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Demographic, clinical, and laboratory characteristics of study patients Variables Good outcomes (n=922) Adverse outcomes (n=109) p -value Demographic data Age (years) 44 (18-98) 72 (18-99) <0.001 Female / Male 213 / 709 35 / 74 0.037 Etiological Factor Groups, n (%) Group 1 Group 2 Group 3 Group 4 Group 5 184 a (20) 373 a (40.5) 15 a (1.6) 25 a (2.7) 325 a (35.2) 47 b (43.1) 34 a (31.2) 5 b (4.6) 5 a (4.6) 18 b (16.5) <0.001 Laboratory results White blood cell count (x10 3 /μL) 10.89 (0.10-63.79) 11.58 (0.27-46.44) 0.244 Hemoglobin (g/dL) 13.40 (4.20-18.40) 12 (3-18.5) <0.001 Platelet count (x10 3 /μL) 225.5 (4-654) 183 (12-614) <0.001 MCHC (g/dL) 33.3 (25.8-39.9) 32.3 (24.8-37.5) <0.001 MPV (fl) 10.1 (7.8-14) 10.45 (8.5-14.5) <0.001 CK (U/L) 1549 (1000-54,986) 1538 (1000-16,636) 0.666 Glucose (mg/dL) 115 (25-580) 134 (30-1068) <0.001 Urea (mg/dL) 33 (3-408) 98.5 (18-559) <0.001 Creatinine (mg/dL) 0.94 (0.24-6.86) 2.19 (0.41-19.24) <0.001 GFR (mL/min) 90.55 (13.3-172.8) 24.0 (2.0-112.6) <0.001 Sodium (mEq/L) 137 (103-176) 137 (104-167) 0.877 Potassium (mEq/L) 4.2 (1.4-8.5) 4.2 (1.9-8.9) 0.224 Chloride (mEq/L) 100 (63-141) 100 (59-140) 0.355 Calcium (mg/dL) 9.0 (2.7-11.6) 8.0 (4.1-18.6) <0.001 ALP (U/L) 74 (20-719) 91 (25-1329) <0.001 Troponin T (ng/L) 9 (3-283) 91 (5-753) <0.001 D-dimer (ng/mL) 869 (10-9864) 3690 (324-9684) <0.001 pH 7.38 (6.80-7.69) 7.30 (6.80-7.52) <0.001 HCO 3 (mmol/L) 24.2 (3-38.2) 17.3 (3.4-37.8) <0.001 BEecf (mmol/L) -0.1 (-26.5-15) -7.45 (-2.47-14.5) <0.001 Lactat (mmol/L) 1.6 (0.3-15) 2.2 (0.2-15) 0.002 Secondary outcomes Hospital LOS, d 0 (0-143) 6 (1-144) <0.001 ICU admission n (%) 101 (11) 80 (73.4) <0.001 ICU LOS, d 6 (1-123) 5 (1-144) 0.174 Data are expressed as median (minimum.-maximum.) frequency (percentage of the group subjects) for categorical variables unless otherwise indicated. The same letters in the same row indicates similarity between column proportions, different letters in the same row indicates statistical difference between column proportions. Abbreviations:MCHC, mean corpuscular hemoglobin concentration; MPV, mean platelet volüme; CK, creatinine Kinase; GFR, glomerular riltration rate (mL/min/1.73 m²); ALP, alkaline phosphatase; HCO 3 , bicarbonate; BEecf, base excess of extracellular fluid; LOS, length of stay; ICU, intensive care unit. Table 2. Optimal cut-off values and performance metrics for predictors of adverse outcomes Variables Cut-off AUC (95% CI) SE AUC Sensitivity (%) Specificity (%) p -value Age (years) ≥50 0.758 (0.731-0.784) 0.023 80.7 59.3 <0.001 Hemoglobin (g/dL) ≤12.5 0.666 (0.636-0.694) 0.029 58.7 64.5 <0.001 Platelet (x10 3 /μL) ≤170 0.633 (0.602-0.662) 0.032 47.7 78.5 <0.001 MCHC (g/dL) ≤32.8 0.680 (0.651-0.709) 0.028 66.6 64.1 <0.001 MPV (fl) ≥10 0.645 (0.614-0.675) 0.028 73.4 47.1 <0.001 Glucose (mg/dL) ≥122 0.596 (0.566-0.627) 0.033 64.2 59.1 0.004 Creatinine (mg/dL) ≥1.5 0.852 (0.829-0.873) 0.021 69.7 85.7 <0.001 GFR (mL/min) ≤50 0.869 (0.846-0.889) 0.019 79.4 82.2 <0.001 Calcium (mg/dL) ≤8,5 0.748 (0.721-0.775) 0.030 66.9 74.9 <0.001 ALP (U/L) ≥115 0.644 (0.614-0.674) 0.032 41.6 87.6 <0.001 Urea (mg/dL) ≥60 0.825 (0.801-0.848) 0.023 70.6 83.4 <0.001 Troponin T (ng/L) ≥30 0.873 (0.843-0.899) 0.018 80.4 73.6 <0.001 D-dimer (ng/mL) ≥1100 0.753 (0.689-0.811) 0.036 83.3 60.1 <0.001 pH ≤7.30 0.658 (0.620-0.694) 0.034 50.9 79.2 <0.001 BEecf (mmol/L) ≤-6 0.719 (0.676-0.759) 0.035 57.1 81.6 <0.001 HCO 3 (mmol/L) ≤18 0.709 (0.672-0.744) 0.033 53.0 83.1 <0.001 Lactat (mmol/L) ≥2 0.606 (0.561-0.650) 0.035 54.1 63.1 0.002 Abbreviations: MCHC, mean corpuscular hemoglobin concentration; MPV, mean platelet volüme; GFR, glomerular filtration rate (mL/min/1.73 m²); ALP, alkaline phosphatase; HCO3, bicarbonate; BEecf, base excess of extracellular fluid; AUC, area under the curve; CI, confidence interval; SE AUC , standard error of the area under the curve. Table 3. Multivariate logistic regression analysis results for predictors of adverse outcomes Predictors Multivariate LR β/minimum β Allocated point β OR (95% CI) p -value Age, years (Reference: 170) Platelet ≤170 0.694 2.001 (1.122-3.567) 0.019 0.694/0.584 1 MCHC, g/dL (Reference: >32.8) MCHC ≤32.8 0.584 1.793 (1.004-3.202) 0.048 0.584/0.584 1 Calcium, mg/dL (Reference: >8.5) Calcium ≤8.5 0.678 1.971 (1.095-3.548) 0.024 0.678/0.584 1 ALP, U/L (Reference: -6) BEecf ≤-6 1.496 4.463 (2.458-8.103) <0.001 1.496/0.584 2.5 Etiological factor groups (Reference: Group 5) Group 1 Group 2 Group 3 Group 4 0.925 -0.095 1.440 0.717 2.522 (1.167-5.450) 0.910 (0.394-2.101) 4.220 (1.117-15.942) 2.049 (0.372-11.291) 0.014 0.019 0.824 0.034 0.410 0.925/0.584 0/0.584 1.440/0.584 0/0.584 1.5 0 2.5 0 Forward Wald method was used as variable selection to multivariate LR model Abbreviations: MCHC, mean corpuscular hemoglobin concentration; ALP, alkaline phosphatase;BEecf, base excess of extracellular fluid; OR, odds ratio; CI, confidence interval; Multivariate LR, multivariate linear regression Table 4. Mugla Score for predicting adverse outcomes Variables Allocated point a Age ≥ 50 years 1.5 Platelet ≤ 170 x10 3 /μL 1 MCHC ≤ 32.8 g/dL 1 Calcium ≤ 8.5 mg/dL 1 ALP ≥ 115 U/L 1.5 BEecf ≤ -6 mmol/L 2.5 Etiological factor groups b Group 1 1.5 Group 3 2.5 a , A total score individual patient score is obtained by summing the points for each variables b , Group 1 , Alcohol consumption, endocrine disorders, metabolic disturbances, myopathies and infections; Group 3, Hyperosmolar and dehydration conditions Abbreviations:ALP, Alkaline phosphatase; MCHC, Mean Corpuscular Hemoglobin Concentration;BEecf, base excess of extracellular fluid; Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Internal and Emergency Medicine → Version 1 posted Reviewers agreed at journal 20 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Editor assigned by journal 17 Apr, 2025 First submitted to journal 17 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6185742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444437743,"identity":"075ad43f-426a-4863-8c94-4ddd9c97dbce","order_by":0,"name":"Omer Faruk Karakoyun","email":"","orcid":"","institution":"TC Sağlık Bakanlığı Muğla Eğitim ve Araştırma Hastanesi: TC Saglik Bakanligi Mugla Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Omer","middleName":"Faruk","lastName":"Karakoyun","suffix":""},{"id":444437744,"identity":"5f557d48-258f-45c6-94ea-64e528903e58","order_by":1,"name":"Fulden Cantaş Türkiş","email":"","orcid":"","institution":"Muğla Sıtkı Koçman Üniversitesi: Mugla Sitki Kocman Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Fulden","middleName":"Cantaş","lastName":"Türkiş","suffix":""},{"id":444437745,"identity":"652d2d7c-d8a2-4ea3-b106-a78092c5e2a9","order_by":2,"name":"Yalcin Golcuk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFAC5gaGBCDFB2QxfACLJBDSwgjRwgbUwjgDpuUAIS0MUC3MPMRoMTh+sPHDwx21cmzsZ4yNbXMOM/Cz5xgwf9yDR8uZxGaJxDPHjdl4coyTc7cdZpDseWPAcOAZHi0HEtsYEtuOJbZJ8BgfBmkxuJED1ILHZQbnHyJpsQRqsSeo5QbYlhqwlmRGkC0SBLRI3ngI9EvbAaBf0ooNe7el80iceVZw4AweLXznkw9+/NlWJ8fPfnizxM9t1nL87ckbH1Tg0aIAkTsMFwBHDR4NDAzyDWCqDp+aUTAKRsEoGOkAAA6hVYqV4a1sAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8530-8607","institution":"Muğla Sıtkı Koçman Üniversitesi: Mugla Sitki Kocman Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Yalcin","middleName":"","lastName":"Golcuk","suffix":""},{"id":444437746,"identity":"b2f465df-52e9-4ca5-a25c-d82d5c802c88","order_by":3,"name":"Mehmet Reha Yılmaz","email":"","orcid":"","institution":"Coventry and Warwickshire Hospital: Coventry and Warwickshire Partnership NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Reha","lastName":"Yılmaz","suffix":""},{"id":444437747,"identity":"4f0c73e5-e155-4f69-b824-47c776566373","order_by":4,"name":"Burcu Kaymak Golcuk","email":"","orcid":"","institution":"TC Sağlık Bakanlığı Muğla Eğitim ve Araştırma Hastanesi: TC Saglik Bakanligi Mugla Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Burcu","middleName":"Kaymak","lastName":"Golcuk","suffix":""}],"badges":[],"createdAt":"2025-03-08 20:08:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6185742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6185742/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-025-04009-y","type":"published","date":"2025-06-11T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81011995,"identity":"a947649e-2f6e-474b-9db5-243cd574ca50","added_by":"auto","created_at":"2025-04-21 08:26:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35715,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram depicting patient selection for inclusion in the final analysis.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185742/v1/4b26d93486485f915760c6f0.jpg"},{"id":81013191,"identity":"01bd2949-04c3-4ae8-bb75-f57a5186188c","added_by":"auto","created_at":"2025-04-21 08:34:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41140,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the roc curve for the Mugla Score in predicting 3-month adverse outcomes\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6185742/v1/4adab79de7c9199096676aac.jpeg"},{"id":81014574,"identity":"a8637115-953d-4dca-8acc-5f6f17b1b9c9","added_by":"auto","created_at":"2025-04-21 08:42:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96389,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve for predicted probabilities against observed outcomes\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6185742/v1/238986828f64eef30d787cc6.jpeg"},{"id":84726537,"identity":"2f20f721-f1a9-4dbf-883e-64fd4d018bc0","added_by":"auto","created_at":"2025-06-16 16:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6185742/v1/2b56f2c5-025e-4e71-8355-6e9b7dd432e7.pdf"}],"financialInterests":"","formattedTitle":"Development of the Mugla Score: An Association-Based Tool for Risk Stratification in Emergency Department Patients with Rhabdomyolysis","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eRhabdomyolysis is a complex clinical syndrome defined by the pathological release of intracellular constituents into the systemic circulation secondary to skeletal muscle injury {Stanley M, \u0026nbsp;In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing`; 2024 Jan-. \u0026nbsp; #1}{Stanley M, \u0026nbsp;In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing`; 2024 Jan-. \u0026nbsp; #1}. This syndrome can be precipitated by a myriad of etiologies, both traumatic and non-traumatic in nature. Diagnostic confirmation is typically achieved when serum creatine phosphokinase (CK) levels exceed 1000 U/L, a pivotal biomarker indicative of extensive myocyte destruction. The underlying pathophysiology involves the disintegration of muscle fibers, culminating in the efflux of myoglobin, electrolytes, and other intracellular components into the bloodstream. This cascade of events can precipitate a diverse array of clinical outcomes, ranging from benign asymptomatic CK elevation to severe, potentially life-threatening complications such as acute kidney injury (AKI) and, in extreme cases, mortality [1,2].\u003c/p\u003e\n\u003cp\u003eRhabdomyolysis presents significant clinical challenges for Emergency Physicians (EPs) due to its potential for rapid progression to life-threatening complications if not promptly recognized and managed [3]. Among these complications, AKI is particularly concerning, as it frequently necessitates renal replacement therapy (RRT). Although several scoring systems exist for predicting complications, there remains a critical need for more comprehensive models that incorporate both etiological and laboratory data to enhance the prediction of adverse outcomes, including RRT and mortality [4,5]. In response to these challenges, we aimed to develop a novel scoring system specifically designed for the Emergency Departments (EDs), facilitating early identification of patients at high risk for both requiring RRT and all-cause mortality.\u003c/p\u003e"},{"header":"2.\tMethods","content":"\u003cp\u003e\u003cem\u003e2.1. Study Design and Setting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective, single-center, cross-sectional cohort study was conducted at the ED of a university-affiliated training and research hospital in [\u003cem\u003eblinded for peer review\u003c/em\u003e], Turkey, from July 1, 2019, to July 1, 2024. The hospital, with a 50-bed ED and approximately 150,000 annual visits, offered an ideal setting for developing rhabdomyolysis assessment tools. Ethical approval was granted by the Institutional Review Board of [\u003cem\u003eblinded for peer review\u003c/em\u003e] University (decision number: 240035/12). The study adhered to the principles outlined in the Declaration of Helsinki. Due to the retrospective nature of the analysis, the requirement for obtaining written informed consent from patients was waived.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Selection of Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria consisted of all patients aged 18 years or older who were diagnosed with rhabdomyolysis, confirmed by serum CK levels exceeding 1000 IU/L upon admission to the ED. The exclusion criteria included patients younger than 18 years, those with elevated CK levels due to acute coronary syndrome, cerebrovascular infarction, or bleeding, as well as patients who developed rhabdomyolysis post-admission from pre-existing conditions or iatrogenic causes. Additionally, patients with end-stage renal disease requiring RRT, those transferred out of the hospital, lost to follow-up, or with incomplete data were excluded. As this study retrospectively included all eligible patients during the specified period, a formal power analysis was not performed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Definitions and Determination Etiological Factor Groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRhabdomyolysis\u003c/em\u003e was defined by a serum CK level exceeding 1000 IU/L, indicative of significant muscle injury. \u003cem\u003eRenal replacement therapy\u003c/em\u003e encompassed peritoneal dialysis, hemodialysis, or continuous RRT. \u003cem\u003eAdverse outcomes\u003c/em\u003e were defined as the the initiation of RRT or all-cause mortality within a 3-month period.\u003c/p\u003e\n\u003cp\u003eRhabdomyolysis is a multifactorial condition with numerous potential etiologies, and currently, no universally accepted classification system exists. In our study, etiological factors were systematically categorized into five distinct classifications to enhance the clarity and precision of our analysis [1,6]. \u003cem\u003eGroup 1\u003c/em\u003e encompassed etiological factors such as alcohol consumption, endocrine abnormalities, metabolic derangements, myopathies, and infections, including COVID-19. \u003cem\u003eGroup 2\u003c/em\u003e involved trauma-related causes, surgical procedures, seizure activity, and extreme physical exertion. \u003cem\u003eGroup 3\u003c/em\u003e encompassed hyperosmolar conditions, specifically general medical states associated with dehydration, including prolonged exposure to elevated temperatures and insufficient fluid intake. \u003cem\u003eGroup 4\u003c/em\u003e comprised rhabdomyolysis induced by pharmacologic agents, exposure to various venoms, including those from snakes and scorpions, as well as environmental toxins. \u003cem\u003eGroup 5\u003c/em\u003e encompassed factors that did not fit within the predefined categories and were thus classified as \u0026quot;other.\u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Data Collection\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePotentially eligible patient encounters were identified through a comprehensive query of the records database at [\u003cem\u003eblinded for peer review\u003c/em\u003e] Training and Research Hospital. Data collection was meticulously performed using a standardized electronic spreadsheet, ensuring precise and thorough documentation of all pertinent variables. Upon ED admission, data were systematically recorded, including patient demographics and the etiological factors associated with rhabdomyolysis. Additionally, comprehensive ancillary data were gathered, encompassing initial laboratory results (e.g., complete blood count, serum chemistry, and blood gas analysis) as well as clinical seconder outcome measures such as hospital admission status and length of stay (LOS). The composite outcomes, including the need for RRT or 3-month all-cause mortality, were systematically documented for each patient.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe normality of the data was assessed using the Kolmogorov-Smirnov test. Continuous variables were presented as mean \u0026plusmn; standard deviation (SD) for normally distributed data or as median (minimum\u0026ndash;maximum) for non-normally distributed data, while categorical variables were expressed as frequencies and percentages. Comparative analyses between the good and adverse outcome groups were conducted utilizing the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test for continuous variables due to non-normal distribution, while categorical variables were analyzed with the \u0026chi;\u0026sup2; test.\u003c/p\u003e\n\u003cp\u003eTo develop a novel predictive score, initial univariate analyses were conducted to explore the relationships among demographic characteristics, etiological factors, and laboratory parameters. Based on these findings, optimal cut-off values for statistically significant variables were determined using Receiver Operating Characteristic (ROC) analysis, with Youden\u0026rsquo;s index employed to maximize sensitivity and specificity. Subsequently, the statistically significant variables that met the established cut-off values were incorporated into a multivariate logistic regression model, utilizing forward stepwise selection based on the Wald test to identify independent predictors of adverse outcomes. The selection criteria included an entry threshold of \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 and a retention threshold of\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt;0.10, ensuring the robustness of the final model while minimizing the risk of overfitting.\u003c/p\u003e\n\u003cp\u003eConsequently, a novel score was derived from this comprehensive logistic regression model. To enhance clinical interpretability, the regression coefficients of statistically significant predictors were standardized by dividing each coefficient by the smallest significant predictor coefficient and rounding to the nearest 0.5. Non-significant predictors were assigned a score of zero, and the cumulative score for each patient was computed by summing the individual predictor scores, resulting in a clinically relevant scoring system. Following this, ROC curve analysis was conducted to assess the utility of the novel score and to determine the optimal cut-off value. At this threshold, the positive predictive value (PPV) and negative predictive value (NPV) were calculated. In addition to this analysis, the model\u0026apos;s internal validity was further assessed through the application of k-fold cross-validation (k = 5), which provided a robust measure of predictive consistency across multiple subsets of the dataset, thereby strengthening the overall findings of the study. All statistical tests were performed as two-tailed, with a \u003cem\u003ep\u003c/em\u003e-value \u0026lt;0.05 considered statistically significant. Analyses were conducted using SPSS software version 27.0 (IBM Corp., Armonk, NY, USA) for general statistical analyses, MedCalc (MedCalc Software Ltd., Ostend, Belgium) for ROC analysis, and MATLAB (The MathWorks, Inc. MATLAB, version 9.11 (R2021b). Natick, Massachusetts: The MathWorks Inc., 2021) for k-fold cross-validation.\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003eThis study analyzed data from 1,031 patients diagnosed with rhabdomyolysis, with a mean age of 49.0\u0026plusmn; 21.8 years (range: 18\u0026ndash;99 years). The majority of the participants were male, comprising 75.9% of the population. The predominant etiological groups linked to rhabdomyolysis were Group 2 (39.4%) and Group 5 (33.2%). Composite adverse outcomes occurred in 109 patients, representing 10.6% of the cohort. Of the entire cohort, 56 patients (5.5%) required RRT, and 84 patients (8.1%) succumbed within the 3-month follow-up period. Notably, the median survival time for those who died was 4 days (range: 1\u0026ndash;78 days). Patients with adverse outcomes had a significantly higher median age of 72 years (18\u0026ndash;99) compared to 44 years (18\u0026ndash;98) in those with good outcomes (p \u0026lt;0.001). The median CK level for the entire cohort was 2627 U/L (range: 1000\u0026ndash;54,986 U/L). There was no statistically significant difference in CK levels between outcome groups: 1549 U/L (1000\u0026ndash;54,986 U/L) for good outcomes and 1538 U/L (1000\u0026ndash;16,636 U/L) for adverse outcomes (p = 0.666). The hospitalization rate among patients was 53.1% (n = 545), with 14.4% (n = 148) of these requiring admission to the ICU. The median LOS for hospitalized patients was 7 days (range: 1\u0026ndash;144 days), while the median LOS for ICU admissions was 6 days (range: 1\u0026ndash;144 days). Table 1 provides a comprehensive overview of the baseline demographic characteristics, etiological factors, laboratory parameters, and secondary outcomes of the study population, stratified by outcome status at the end of the 3-month follow-up.\u003c/p\u003e\n\u003cp\u003eTo construct a predictive score, univariate analyses were initially conducted to identify significant variables associated with adverse outcomes. Subsequently, ROC curve analysis was employed to establish optimal cut-off values for the identified candidate predictors, with results summarized in Table 2. In the following phase, a multivariate logistic regression model was developed, with details presented in Table 3. This final model integrated several variables: age (\u0026ge;50 years), platelet count (\u0026le;170 x 10\u0026sup3;/\u0026mu;L), Mean Corpuscular Hemoglobin Concentration (MCHC) (\u0026le;32.8 g/dL), calcium levels (\u0026le;8.5 mg/dL), Alkaline Phosphatase (ALP) levels (\u0026ge;115 U/L), Base Excess of extracellular fluid (BEecf) (\u0026le;-6 mmol/L), and relevant etiological factors from Groups 1 and 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe final model, named the Mugla Score in reference to the location of its development, provides a comprehensive assessment of patient risk. The score ranges from 0 to 12.5, with the specific variables and their respective point allocations outlined in Table 4. ROC analysis determined that a Mugla Score of \u0026ge;4 was optimal for predicting adverse outcomes, exhibiting a sensitivity of 75.0%, specificity of 75.3%, and an AUC of 0.861 (95% CI: 0.824\u0026ndash;0.898;\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt;0.001) as shown in Figure 1. At this threshold, the score demonstrated a PPV of 39% and a NPV of 97% (\u003cem\u003ep\u003c/em\u003e \u0026lt;0.001). The internal validity of the model was further assessed through 5-fold cross-validation, resulting in an AUC of 0.833 (95% CI: 0.796-0.866; \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001), with a sensitivity of 92.77% (95% CI: 84.9-97.3%) and a specificity of 64.84% (95% CI: 59.8-69.6%). Additionally, the PPV was 36.32%, while the NPV was 97.65%.\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eThis study introduces the Mugla Score, a prognostic tool designed to predict adverse outcomes in patients with rhabdomyolysis in the ED. Incorporating demographic factors, such as age, and etiological considerations alongside key laboratory parameters\u0026mdash;including platelet count, MCHC, calcium levels, ALP, and BEecf\u0026mdash;the score exhibits substantial clinical utility in predicting the need for RRT and assessing three-month mortality risk. The Mugla Score provides a robust framework for identifying high-risk rhabdomyolysis patients, enhancing clinical decision-making and enabling targeted interventions to improve outcomes.\u003c/p\u003e\n\u003cp\u003eResearch on the impact of CBC parameters on adverse outcomes in rhabdomyolysis is limited. However, the simultaneous decrease in platelet count and MCHC indicates complex pathophysiological mechanisms. The link between ferrihemate, an endogenous breakdown product of myoglobin, and reductions in these parameters is primarily indirect, resulting from oxidative stress and systemic inflammation rather than direct effects. Under acidic conditions, ferrihemate generates free radicals that lead to oxidative damage and inflammation, which can activate and consume platelets, causing thrombocytopenia [7,8]. Similarly, reduced MCHC levels are associated with oxidative stress and hypoxia, impacting red blood cell stability and hemoglobin synthesis [9]. This instability can result in hemolysis, further altering MCHC and reflecting the extent of inflammation and cellular injury. Thus, monitoring platelet count and MCHC in patients with rhabdomyolysis may provide important insights into disease severity and the risk of AKI, highlighting their role as indicators of systemic complications.\u003c/p\u003e\n\u003cp\u003eThe association between ALP levels and rhabdomyolysis is receiving growing attention, with significant implications for both prognostic assessment and therapeutic strategy [10,11]. Elevated ALP shows a strong correlation with rhabdomyolysis severity, supporting its inclusion in the Mugla Score as a biomarker of disease burden. The pathophysiological basis for ALP elevation in this context is likely multifactorial, involving muscle cell injury, metabolic disturbances, and systemic inflammatory responses [12]. Additionally, metabolic dysregulation and oxidative stress, commonly observed in rhabdomyolysis, may further increase ALP levels, possibly reflecting a hepatic response to systemic injury. Hypocalcemia, also included in the Mugla Score, may result from calcium binding to myoglobin and other proteins released during muscle breakdown. Elevated ALP and low calcium levels may further indicate systemic inflammation and tissue hypoxia, both of which are prevalent in severe rhabdomyolysis cases [13, 14]. Incorporating both ALP and hypocalcemia into clinical assessments could thus improve early therapeutic decision-making, enable the anticipation of complications, and ultimately enhance patient outcomes in the ED.\u003c/p\u003e\n\u003cp\u003eThe relationship between acid-base disturbances, particularly metabolic acidosis, and rhabdomyolysis is crucial in predicting patient outcomes, as metabolic acidosis is consistently linked to poor prognosis in critically ill patients, a well-documented association in the literat\u0026uuml;re [15, 16]. A reduction in base excess serves as a robust predictor of adverse outcomes in rhabdomyolysis, highlighting the necessity for prompt identification and correction of metabolic derangements. Integrating acid-base parameters into the Mugla Score augments its prognostic precision, emphasizing the pivotal role of metabolic correction in optimizing clinical outcomes. This approach facilitates the early detection of potential complications which can significantly improve patient recovery trajectories.\u003c/p\u003e\n\u003cp\u003eIn contrast to previous studies, our analysis did not identify trauma-related etiologies\u0026mdash;commonly associated with direct muscle injury\u0026mdash;as significant predictors of adverse outcomes in rhabdomyolysis [4, 17]. Instead, dehydration emerged as a key determinant in the Mugla Score, contributing to a higher points and reflecting its significant prognostic value. This finding aligns with existing literature linking volume depletion to worse outcomes in rhabdomyolysis, emphasizing dehydration\u0026apos;s detrimental impact on renal function and systemic stability. Early fluid management, therefore, plays a critical role in mitigating the adverse effects of dehydration and improving patient outcomes. Furthermore, factors such as alcohol consumption, endocrine disorders, metabolic disturbances, myopathies, and infections\u0026mdash;commonly recognized in the literature as contributors to rhabdomyolysis\u0026mdash;were included as predictors of adverse outcomes [18-22]. These associations reinforce the multifactorial etiology of rhabdomyolysis, underscoring the importance of comprehensive patient evaluation for effective risk stratification. The adaptability of the Mugla Score to diverse clinical scenarios further enhances its utility in EDs, facilitating more precise risk prediction and management across varied patient populations. This flexibility is particularly relevant given recent global health challenges, notably the COVID-19 pandemic, which had a significant impact on our study population and period. The increased strain on healthcare systems, compounded by a higher incidence of related complications, underscores the urgent need for adaptive prognostic models that account for the broad spectrum of etiological factors influencing rhabdomyolysis.\u003c/p\u003e\n\u003cp\u003eThe McMahon Score, focused on patients with initial CK levels exceeding 5,000 U/L, is widely recognized as a cornerstone in the prognostic evaluation of rhabdomyolysis, demonstrating robust predictive accuracy for the need for RRT and mortality [4]. However, its utility remains largely restricted to those with markedly elevated CK levels, potentially overlooking a subset of patients at significant risk for adverse outcomes despite presenting with lower CK values. The Mugla Score has several strengths and the potential for broad acceptance, offering a significant advancement over existing models by providing a more comprehensive and nuanced approach to prognostication in rhabdomyolysis. By incorporating a diverse array of clinical parameters, it facilitates the assessment of a broader spectrum of disease severity, effectively including patients with CK levels below 5,000 U/L.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides a valuable advancement in rhabdomyolysis prognosis through the development of the Mugla Score, yet it is important to acknowledge its limitations. First, the retrospective, single-center design may constrain the broader applicability of these findings. Second, the Mugla Score could not be validated in external patient populations; however, to ensure internal validity, we employed stratified k-fold cross-validation, a robust method for internal model validation. Third, the variability in timing of patient presentation following injury, along with the lack of routine CK measurements, may have resulted in both underestimation and undiagnosed cases of rhabdomyolysis, potentially influencing the predictive accuracy of our model.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eThe Mugla Score, a newly developed and internally validated prognostic tool, offers a substantial advancement in assessing rhabdomyolysis outcomes, providing an effective framework for predicting adverse events among patients presenting to the ED. With an established cutoff score of 4, this tool accurately identifies patients at elevated risk of severe complications, including the need for RRT and three-month mortality. Multicenter studies and prospective cohort analyses are warranted to externally validate this score and confirm its applicability across varied clinical settings and patient populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOmer Faruk Karakoyun (0000-0002-4476-7989), Fulden Cantas Turkis (0000-0002-7018-7187), Yalcin Golcuk (0000-0002-8530-8607),\u0026nbsp;Mehmet Reha Yılmaz (0009-0005-9152-0662), and\u0026nbsp;Burcu Kaymak Golcuk (0000-0003-2293-697X) contributed to this study as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e Omer Faruk Karakoyun, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSoftware:\u003c/strong\u003e Burcu Kaymak Golcuk,\u0026nbsp;Mehmet Reha Yılmaz\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eValidation:\u003c/strong\u003e Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFormal analysis:\u003c/strong\u003e Omer Faruk Karakoyun,\u0026nbsp;Mehmet Reha Yılmaz, Fulden Cantas Turkis, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInvestigation:\u003c/strong\u003e Omer Faruk Karakoyun,\u0026nbsp;Mehmet Reha Yılmaz,\u0026nbsp;Burcu Kaymak Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResources:\u003c/strong\u003e Yalcin Golcuk,\u0026nbsp;Burcu Kaymak Golcuk\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData Curation:\u003c/strong\u003e Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWriting - Original Draft:\u003c/strong\u003e Omer Faruk Karakoyun,\u0026nbsp;Burcu Kaymak Golcuk, Yalcin Golcuk,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWriting - Review \u0026amp; Editing:\u003c/strong\u003e Omer Faruk Karakoyun, Fulden Cantas Turkis, Yalcin Golcuk,\u0026nbsp;Mehmet Reha Yılmaz,\u0026nbsp;Burcu Kaymak Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVisualization:\u003c/strong\u003e Omer Faruk Karakoyun, Halil Emre Koyuncuoglu, Fulden Cantas Turkis, Yalcin Golcuk\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSupervision:\u003c/strong\u003e Yalcin Golcuk\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStanley M, Chippa V, Aeddula NR, et al. Rhabdomyolysis. [Updated 2023 Apr 16]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK448168/.\u003c/li\u003e\n\u003cli\u003eHaroun MW, Dieiev V, Kang J, Barbi M, Marashi Nia SF, Gabr M, et al. Rhabdomyolysis in COVID-19 patients: A retrospective observational study. Cureus 2021;13(1). https://doi.org/10.7759/cureus.12552.\u003c/li\u003e\n\u003cli\u003eLong B, Koyfman A, Gottlieb M. An evidence-based narrative review of the emergency department evaluation and management of rhabdomyolysis. Am J Emerg Med 2019;37(3):518-23. https://doi.org/10.1016/j.ajem.2018.12.061.\u003c/li\u003e\n\u003cli\u003eMcMahon GM, Zeng X, Waikar SS. A risk prediction score for kidney failure or mortality in rhabdomyolysis. JAMA Intern Med 2013;173(19):1821-8. https://doi.org/10.1001/jamainternmed.2013.9774.\u003c/li\u003e\n\u003cli\u003eLiu C, Yuan Q, Mao Z, Hu P, Wu R, Liu X, et al. Development and validation of a model for the early prediction of the RRT requirement in patients with rhabdomyolysis. Am J Emerg Med 2021;46:38-44. https://doi.org/10.1016/j.ajem.2021.03.006.\u003c/li\u003e\n\u003cli\u003eBhai S, Dimachkie MM, Targoff IN, Shefner JM, Dashe JF. Rhabdomyolysis: Epidemiology and etiology. UpToDate. Last updated July 2024. Available at: https://www.uptodate.com/contents/rhabdomyolysis-epidemiology-and-etiology. Accessed November 10, 2024.\u003c/li\u003e\n\u003cli\u003eDe Guzman MM. Rhabdomyolysis. Medscape. Available at: https://emedicine.medscape.com/article/1007814-overview#a3. Accessed November 10, 2024.\u003c/li\u003e\n\u003cli\u003eGaut JP, Liapis H. Acute kidney injury pathology and pathophysiology: A retrospective review. Clin Kidney J 2020;14(2):526-36. https://doi.org/10.1093/ckj/sfaa142.\u003c/li\u003e\n\u003cli\u003eGrivei A, Giuliani KTK, Wang X, Ungerer J, Francis L, Hepburn K, et al. Oxidative stress and inflammasome activation in human rhabdomyolysis-induced acute kidney injury. Free Radic Biol Med 2020;160:690-5. https://doi.org/10.1016/j.freeradbiomed.2020.09.011.\u003c/li\u003e\n\u003cli\u003eLim AK. Abnormal liver function tests associated with severe rhabdomyolysis. World J Gastroenterol 2020;26(10):1020-8. https://doi.org/10.3748/wjg.v26.i10.1020.\u003c/li\u003e\n\u003cli\u003eLaitselart P, Derely J, Daban JL, De Rudnicki S, Libert N. Relationship between creatine kinase and liver enzymes in war wounded with rhabdomyolysis. Injury 2022;53(1):166-70. https://doi.org/10.1016/j.injury.2021.10.004.\u003c/li\u003e\n\u003cli\u003eTorino C, Mattace-Raso F, van Saase JL, Postorino M, Tripepi GL, Mallamaci F, et al. Oxidative stress as estimated by gamma-glutamyl transferase levels amplifies the alkaline phosphatase-dependent risk for mortality in ESKD patients on dialysis. Oxid Med Cell Longev 2016;2016:8490643. https://doi.org/10.1155/2016/8490643.\u003c/li\u003e\n\u003cli\u003eZhang MH. Rhabdomyolysis and its pathogenesis. World J Emerg Med 2012;3(1):11-5. https://doi.org/10.5847/wjem.j.issn.1920-8642.2012.01.002.\u003c/li\u003e\n\u003cli\u003eHigaki M, Tanemoto M, Shiraishi T, Taniguchi K, Fujigaki Y, Uchida S. Acute kidney injury facilitates hypocalcemia by exacerbating the hyperphosphatemic effect of muscle damage in rhabdomyolysis. Nephron 2015;131(1):11-6. https://doi.org/10.1159/000437391.\u003c/li\u003e\n\u003cli\u003eLentz SA, Ackil D. Metabolic acid-base disorders. Emerg Med Clin North Am 2023;41(4):849-62. https://doi.org/10.1016/j.emc.2023.06.008.\u003c/li\u003e\n\u003cli\u003eAchanti A, Szerlip HM. Acid-base disorders in the critically ill patient. Clin J Am Soc Nephrol 2023;18(1):102-12. https://doi.org/10.2215/CJN.04500422.\u003c/li\u003e\n\u003cli\u003eComoglu M, Acehan F, Inan O, Demir BF, Yılmaz Y, Sahiner ES. A new score predicting renal replacement therapy in patients with crush injuries: Analysis of a major earthquake. Am J Emerg Med 2024;87:1-7. https://doi.org/10.1016/j.ajem.2024.10.031.\u003c/li\u003e\n\u003cli\u003eHtet Z. Alcohol-induced rhabdomyolysis: A disease with potential pitfalls. Acute Med 2018;17(4):226-8.\u003c/li\u003e\n\u003cli\u003eZhou Q, Li B, Tian X. Rhabdomyolysis caused by hypothyroidism: Research progress. Horm Metab Res 2022;54(11):731-5. https://doi.org/10.1055/a-1951-1646.\u003c/li\u003e\n\u003cli\u003eRao A, Nawaz I, Arbi FM, Ishtiaq R. Proximal myopathy: Causes and associated conditions. Discoveries (Craiova) 2022;10(4). https://doi.org/10.15190/d.2022.19.\u003c/li\u003e\n\u003cli\u003ePreger A, Wei R, Berg B, Golomb BA. COVID-19-associated rhabdomyolysis: A scoping review. Int J Infect Dis 2023;136:115-26. https://doi.org/10.1016/j.ijid.2023.09.002.\u003c/li\u003e\n\u003cli\u003eSamardzic T, Muradashvili T, Guirguis S, et al. Relationship between rhabdomyolysis and SARS-CoV-2 disease severity. Cureus 2024;16(1). https://doi.org/10.7759/cureus.53029.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic, clinical, and laboratory characteristics of study patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood outcomes (n=922)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdverse outcomes (n=109)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic data\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e44 (18-98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e72 (18-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eFemale / Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e213 / 709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e35 / 74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEtiological Factor\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Groups,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003en (%)\u003c/p\u003e\n \u003cp\u003eGroup 1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003cp\u003eGroup 4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e184\u003csup\u003ea\u003c/sup\u003e (20)\u003c/p\u003e\n \u003cp\u003e373\u003csup\u003ea\u003c/sup\u003e (40.5)\u003c/p\u003e\n \u003cp\u003e15\u003csup\u003ea\u003c/sup\u003e (1.6)\u003c/p\u003e\n \u003cp\u003e25\u003csup\u003ea\u003c/sup\u003e (2.7)\u003c/p\u003e\n \u003cp\u003e325\u003csup\u003ea\u003c/sup\u003e (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47\u003csup\u003eb\u003c/sup\u003e (43.1)\u003c/p\u003e\n \u003cp\u003e34\u003csup\u003ea\u003c/sup\u003e (31.2)\u003c/p\u003e\n \u003cp\u003e5\u003csup\u003eb\u003c/sup\u003e (4.6)\u003c/p\u003e\n \u003cp\u003e5\u003csup\u003ea\u003c/sup\u003e (4.6)\u003c/p\u003e\n \u003cp\u003e18\u003csup\u003eb\u003c/sup\u003e (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLaboratory results\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eWhite blood cell count (x10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10.89 (0.10-63.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e11.58 (0.27-46.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e13.40 (4.20-18.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e12 (3-18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePlatelet count (x10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e225.5 (4-654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e183 (12-614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eMCHC (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e33.3 (25.8-39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e32.3 (24.8-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eMPV (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10.1 (7.8-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e10.45 (8.5-14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCK (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1549 (1000-54,986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1538 (1000-16,636)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e115 (25-580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e134 (30-1068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eUrea (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e33 (3-408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e98.5 (18-559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.94 (0.24-6.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.19 (0.41-19.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eGFR (mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e90.55 (13.3-172.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e24.0 (2.0-112.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSodium (mEq/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e137 (103-176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e137 (104-167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4.2 (1.4-8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4.2 (1.9-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eChloride (mEq/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e100 (63-141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e100 (59-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCalcium (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e9.0 (2.7-11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e8.0 (4.1-18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e74 (20-719)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e91 (25-1329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e9 (3-283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e91 (5-753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e869 (10-9864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3690 (324-9684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7.38 (6.80-7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e7.30 (6.80-7.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e24.2 (3-38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e17.3 (3.4-37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eBEecf (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.1 (-26.5-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e-7.45 (-2.47-14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eLactat (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.6 (0.3-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.2 (0.2-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHospital LOS, d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0 (0-143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6 (1-144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eICU admission n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e101 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e80 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eICU LOS, d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e6 (1-123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5 (1-144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eData are expressed as median (minimum.-maximum.) frequency (percentage of the group subjects) for categorical variables unless otherwise indicated. The same letters in the same row indicates similarity between column proportions, different letters in the same row indicates statistical difference between column proportions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:MCHC, mean corpuscular hemoglobin concentration; MPV, mean platelet vol\u0026uuml;me; CK, creatinine Kinase;\u003cem\u003e\u0026nbsp;GFR,\u0026nbsp;\u003c/em\u003eglomerular riltration rate (mL/min/1.73 m\u0026sup2;); ALP, alkaline phosphatase; HCO\u003csub\u003e3\u003c/sub\u003e, bicarbonate; BEecf, \u0026nbsp;base excess of extracellular fluid; LOS, length of stay; ICU, intensive care unit.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Optimal cut-off values and performance metrics for predictors of adverse outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003csub\u003eAUC\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.758 (0.731-0.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e80.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.666 (0.636-0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePlatelet (x10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.633 (0.602-0.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMCHC (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.680 (0.651-0.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMPV (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.645 (0.614-0.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.596 (0.566-0.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e64.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.852 (0.829-0.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e69.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eGFR (mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.869 (0.846-0.889)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e82.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eCalcium (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;8,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.748 (0.721-0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e66.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e74.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.644 (0.614-0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e87.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eUrea (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.825 (0.801-0.848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e83.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.873 (0.843-0.899)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.753 (0.689-0.811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.658 (0.620-0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eBEecf (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.719 (0.676-0.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026le;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.709 (0.672-0.744)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e83.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eLactat (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e0.606 (0.561-0.650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e\u003cem\u003e\u0026nbsp;MCHC, mean corpuscular hemoglobin concentration; MPV, mean platelet vol\u0026uuml;me; GFR, glomerular filtration rate (mL/min/1.73 m\u0026sup2;); ALP, alkaline phosphatase; HCO3, bicarbonate; BEecf, base excess of extracellular fluid; AUC, area under the curve; CI, confidence interval; SE\u003csub\u003eAUC\u003c/sub\u003e, standard error of the area under the curve.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multivariate logistic regression analysis results for predictors of adverse outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate LR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;/minimum \u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllocated point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u0026nbsp;\u003c/strong\u003e(Reference: \u0026lt;50)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Age \u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.323 (1.143-4.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.843/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet, x10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L\u0026nbsp;\u003c/strong\u003e(Reference: \u0026gt;170)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Platelet \u0026le;170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.001 (1.122-3.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.694/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCHC, g/dL\u0026nbsp;\u003c/strong\u003e(Reference: \u0026gt;32.8)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; MCHC \u0026le;32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.793 (1.004-3.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.584/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium, mg/dL\u0026nbsp;\u003c/strong\u003e(Reference: \u0026gt;8.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Calcium \u0026le;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.971 (1.095-3.548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.678/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP, U/L\u0026nbsp;\u003c/strong\u003e(Reference: \u0026lt;115)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; ALP \u0026ge;115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.420 (1.303-4.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.884/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBEecf (mol/L\u0026nbsp;\u003c/strong\u003e(Reference: \u0026gt;-6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; BEecf \u0026le;-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.463 (2.458-8.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.496/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiological factor groups (Reference: Group 5)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Group 1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Group 2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Group 3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Group 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003cp\u003e1.440\u003c/p\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.522 (1.167-5.450)\u003c/p\u003e\n \u003cp\u003e0.910 (0.394-2.101)\u003c/p\u003e\n \u003cp\u003e4.220 (1.117-15.942)\u003c/p\u003e\n \u003cp\u003e2.049 (0.372-11.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.925/0.584\u003c/p\u003e\n \u003cp\u003e0/0.584\u003c/p\u003e\n \u003cp\u003e1.440/0.584\u003c/p\u003e\n \u003cp\u003e0/0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eForward Wald method was used as variable selection to multivariate LR model\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u0026nbsp;\u003c/em\u003e\u003cem\u003eMCHC, mean corpuscular hemoglobin concentration; ALP, alkaline phosphatase;BEecf, base excess of extracellular fluid;\u0026nbsp;\u003c/em\u003e\u003cem\u003eOR, odds ratio; CI, confidence interval; Multivariate LR, multivariate linear regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Mugla Score for predicting adverse outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 463px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllocated point\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u0026ge; 50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003ePlatelet \u0026le; 170 x10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eMCHC \u0026le; 32.8 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eCalcium \u0026le; 8.5 mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eALP \u0026ge; 115 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003eBEecf \u0026le; -6 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 577px;\"\u003e\n \u003cp\u003eEtiological factor groups\u003csup\u003eb\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e, A total score individual patient score is obtained by summing the points for each variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e\u003cem\u003e\u0026nbsp;Group 1 , Alcohol consumption, endocrine disorders, metabolic disturbances, myopathies and infections; Group 3,\u0026nbsp;\u003c/em\u003eHyperosmolar and dehydration conditions\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:ALP, Alkaline phosphatase;\u0026nbsp;\u003c/em\u003e\u003cem\u003eMCHC, Mean Corpuscular Hemoglobin Concentration;BEecf, base excess of extracellular fluid;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"rhabdomyolysis, acute kidney injury, renal replacement therapy, emergency department, scoring system, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6185742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6185742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRhabdomyolysis is a potentially life-threatening syndrome characterized by skeletal muscle breakdown and systemic release of intracellular components, often resulting in acute kidney injury or death. Early risk stratification remains challenging in the emergency department (ED) setting due to heterogeneous presentations and unpredictable outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo develop and internally validate the Mugla Score—a pragmatic, association-based tool for predicting adverse outcomes in ED patients with rhabdomyolysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this retrospective, single-center cohort study, adult ED patients with serum creatine kinase ≥1000 U/L between July 1, 2019, and July 1, 2024, were included. The primary outcome was a composite of renal replacement therapy or 90-day mortality. Multivariable logistic regression identified independent predictors, which were assigned weighted point values. Internal validity was assessed using five-fold cross-validation and 1,000-iteration bootstrap resampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 1,031 patients (mean age: 49.0 ± 21.8 years; 75.9% male), 109 (10.6%) experienced the composite outcome. Seven variables were independently associated with adverse events: age ≥50 years, platelet count ≤170 ×10³/μL, MCHC ≤32.8 g/dL, calcium ≤8.5 mg/dL, ALP ≥115 U/L, BEecf ≤−6 mmol/L, and etiological classification. The Mugla Score (range: 0–12.5) showed strong discrimination (AUC: 0.861, 95% CI: 0.824–0.898). A threshold of ≥4 points yielded a 97% negative predictive value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe Mugla Score provides a clinically interpretable, ED-focused tool for early risk stratification in rhabdomyolysis. While internally validated, external prospective studies are needed to assess generalizability prior to routine clinical adoption.\u003c/p\u003e","manuscriptTitle":"Development of the Mugla Score: An Association-Based Tool for Risk Stratification in Emergency Department Patients with Rhabdomyolysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:26:01","doi":"10.21203/rs.3.rs-6185742/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-20T04:29:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-17T13:55:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T12:49:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2025-04-17T08:39:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e834b847-dac0-428b-85d0-14e7234d18cb","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:02:20+00:00","versionOfRecord":{"articleIdentity":"rs-6185742","link":"https://doi.org/10.1007/s11739-025-04009-y","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2025-06-11 15:57:49","publishedOnDateReadable":"June 11th, 2025"},"versionCreatedAt":"2025-04-21 08:26:01","video":"","vorDoi":"10.1007/s11739-025-04009-y","vorDoiUrl":"https://doi.org/10.1007/s11739-025-04009-y","workflowStages":[]},"version":"v1","identity":"rs-6185742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6185742","identity":"rs-6185742","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}